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Record W2940702837

Housing Instability and Concurrent Substance use and Mental Health Concerns: An Examination of Canadian Youth.

2017· article· en· W2940702837 on OpenAlexaffabout
Tayla Smith, Lisa D. Hawke, Gloria Chaim, Joanna Henderson

Bibliographic record

VenuePubMed · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsOutreachMental healthAddictionPsychologyEnvironmental healthPsychiatryMedicinePolitical science
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: Though previous research has identified the high burden of mental health and addiction (MHA) concerns among precariously housed youth, earlier studies have not examined differences in MHA concerns among housing insecure Canadian youth across sectors. This study examines this issue using the Global Appraisal of Individual Needs Short Screener (GAIN-SS) in a cross-sectoral sample of Canadian youth. METHOD: A total of 2605 youth ages 12 to 24 seeking services across sectors completed the GAIN-SS and a sociodemographic form. The analyses described demographic variables and sector of presentation, then evaluated internalizing, externalizing, substance use, and crime/violence concerns based on housing status. RESULTS: < 0.01), which was largely driven by the high rate of concurrent disorders among precariously housed females. CONCLUSIONS: Since precariously housed youth with multiple clinical needs presented across sectors, attention must be given to screening for both housing stability and MHA and building stronger cross-sectoral partnerships. The findings should encourage systematic screening, MHA training and capacity building within housing sectors as well as integrated services across all youth-serving organizations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.005
Science and technology studies0.0040.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.223
GPT teacher head0.397
Teacher spread0.174 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations22
Published2017
Admission routes2
Has abstractyes

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